Improving the Quality and Utility of Electronic Health Record Data through Ontologies
Abstract
:1. Purpose of This Document
- The creation of EHR data, which are more accurate, represent patient and care-related phenomena with greater precision and faithfulness, and are more effectively computable;
- The support of user interfaces that facilitate standard data entry support dynamic data checking and provide improved data retrieval and data analysis, thus warranting a closer match (i) between what the user intends to record and what was actually recorded and (ii) between the user’s information need and its fulfillment;
- The improvement of interoperability of healthcare systems, thus providing a more comprehensive body of clinical data from heterogeneous sources that can not only support clinical decisions, improve quality of care, and avoid medical errors but also enable more advanced digitally based testing of clinical and translational research hypotheses.
- To educate all research and healthcare communities about what can be carried out to enhance and improve the value and usability of EHRs with a particular focus on translational research.
- To educate the following communities about what can be carried out to improve the quality and utility of clinical data by enhancing the usefulness of EHRs (and also using parallel strategies, genomic and other clinically relevant data) at the point of care without increasing the data and documentation burden:
- ○
- Designers and administrators of EHRs;
- ○
- Designers and administrators of Clinical Trial Management Systems;
- ○
- Communities concerned with setting standards for healthcare data;
- ○
- Researchers who conduct clinical studies using EHR data;
- ○
- Health system leadership of CTSA hubs;
- ○
- CTSA principal investigators;
- ○
- National Center for Advancing Translational Sciences (NCATS) staff;
- ○
- Members of government agencies with a stake in making available and using quality clinical data for translational research.
- To lay out opportunities for meaningful next steps, especially as they concern coordination among CTSA hubs.
- To provide examples of what has already been achieved.
- Accuracy: values are correct and valid;
- Comprehensiveness: all data required for a given purpose are included;
- Consistency: the value of the data is reliable and the same across applications;
- Currency: the value of a datum is current for a specific point in time;
- Definitions: clear definitions assure that current and future users will know what the data means;
- Granularity: attributes and values are defined at the correct level of detail;
- Precision: data values are exact enough to support the application;
- Relevancy: data are meaningful to the performance of the application for which they are collected;
- Timeliness: timeliness is determined by how the data are being used.
2. Introduction
2.1. Problem Statement
- There is a failure of human-system interoperability deriving from the fact that the current layout and functionality of digital tools not only create a barrier to fulfilling routine documentation and communication but also lead to poor documentation and require clinicians to spend more time to correct or improve the documentation than expected.
- As cumbersome computer interfaces have taken the place of established modes of communicating and documenting based on paper, phone, and fax to a degree, they bear the risk of increasingly replacing the patient as the principal object of focus during a clinical encounter. As a result, a clinician spends less time with the patients.
- There is a failure of the system–system interoperability—initially advocated as a main rationale for the introduction of EHR technology—however, in reality, any given EHR system is often not even interoperable with the computer systems across the same enterprise.
2.2. The Role of Ontologies with Computable Semantics for the Improvement of Clinical Data Quality
- Data acquisition, including data processing and storage, is constantly supported by appropriate terminology linked to an ontology-based semantic layer, clinical processes, and users. From this layer, we should expect the following:
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- Personalized interfaces that minimize the number of actions required for a given task.
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- Intuitive guidance at data entry, which includes the detection of redundant or erroneous entries.
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- Context-sensitivity that ensures that only the data needed in a given scenario are, in fact, provided by the system.
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- Adjustment of the system to the user’s communication behavior, including the sublanguages used.
- ○
- Understanding of semistructured free text and voice input.
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- Semantic error-detection and alerting approaches [12].
- Supported and enriched by the semantic layer, normalized content ironing out the variability of data input. This requires the following:
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- Information to be stored in a standardized and ontology-aware way.
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- Addressing the requirements for structured information use and reuse for a variety of use cases.
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- Flexibility by ensuring that data mimic as far as possible the structure of reality.
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- Constant monitoring of all actions, with log data feeding a learning system (see Box 3), which aims at optimizing processes and underlying resources.
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- Explicitness of each data element regarding its context and provenance.
- Data reuse enabled by ontology-based data query should address the following aspects:
- ○
- User-friendly, self-explaining query interfaces, which facilitate semantic cross-linkage of patient-related information with general knowledge such as clinical guidelines.
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- Push and pull scenarios that address the information needs of different user groups (clinicians, researchers, administrators, data managers, and patients) and data management tasks need to be supported.
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- Improved reuse of data for research, which includes the interoperation of hitherto separated resources, e.g., EHR with clinical trial management and data capture systems, as well as electronic case report forms.
- ○
- Powerful semantic interoperability, which includes that context is provided when data are exported, as well as the meaning-preserving flow of information between different systems within and between different institutions, jurisdictions, and language groups [13].
3. Glossary
4. The Role of Semantic Standards and Specifications
- (i)
- Terminologies;
- (ii)
- Ontologies;
- (iii)
- Information models;
- (iv)
- Detailed clinical models;
- (v)
- Process and guideline models.
4.1. Terminologies
4.2. Formal Ontologies
4.3. Detailed Clinical Models
4.4. Guideline and Process Models
4.5. Interfaces or Mappings between Different Types of Standards and Specifications
4.5.1. Interfaces or Mappings between Reference Terminologies and User Interface Terminologies
4.5.2. Interfaces or Mappings between Reference Terminologies and Ontologies
4.5.3. Interfaces or Mappings between Ontologies and Aggregation Terminologies
4.5.4. Interfaces between Ontologies and Clinical Models
4.5.5. Clinical Guideline Specification and Process Models
5. Human–Computer Interaction and Usability in EHR System Design
- The training and skill of the users;
- The implementation of specific systems in specific settings;
- The history of human interface technology used in any setting and by any user;
- The relationship of a specific system to the other IT systems with which it must interact;
- The physical environment (e.g., lighting, noise levels, and quality of display screens).
Clinical Decision Support
6. Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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EHR Purposes | EHR Requirements |
---|---|
“Classical”
|
|
Problems Observed in Certain EHR Instances | Characteristics of These Problems |
---|---|
EHR data are inaccurate | EHR data contain too many errors—from carelessly written texts, copy-and-paste errors to clinical coding [8]. |
EHR data are difficult orimpossible to interpret | Complete interpretation of EHR content requires hidden contexts to be made explicit. |
EHR data tell an incomplete story | Missing data elements abound. |
EHR data across organizations are inconsistent | Patients are treated in many places; different organizations’ EHR typically records different information on the same patient; even within the same organization and with the same EHR system, data on a given patient may be inconsistent because of different installed features or different levels of training. |
EHR data are tilted toward the needs of billing and administration | Much data derives from coding of diagnoses and procedures for billing and exhibits even greater inaccuracy than EHR data proper. This is particularly problematic where insurance companies require a specific diagnosis to be present in order to pay for specific procedures, medications, or other treatments. |
EHR data include too much free text | The vast majority of the patient’s story is told in narrative text, but natural language processing (NLP) technology is still too far from perfect for routine use. NLP is complex to set up, use, and maintain. To work well, NLP solutions need to be tailored to document types, domains, and sometimes specific healthcare providers. The quality of NLP solutions critically depends on large training resources, which are expensive to create and are often not available due to privacy concerns. |
her data lack provenance | EHRs are constantly fed by external information systems (e.g., lab systems, connected devices), but they do not always indicate the provenance (source systems and organizations) of these data. |
EHR data are too coarse-grained for research | Coding for billing is at the level of diagnosis categories, not fine-grained diagnoses. Besides imparting loss of information, heterogeneous sets of diseases or procedures are identified by the same code. |
EHR data are derived from clinical care and lack of granularity for research purposes | Clinical care rarely matches the level of rigor in measurement, calibration, and data collection that is required for clinical research. |
Term | Definition | Note |
---|---|---|
representational unit (RU) | A language-independent denotator that corresponds to a node in a terminology or ontology. | RUs are typically identified by a code and a human-readable label. Optionally, their meaning is described by scope notes and by textual and logic-based elucidations or definitions. |
term | A linguistic expression (ranging from a single word to a phrase) that belongs to a domain-specific vocabulary in a given natural language. | We also distinguish labels from terms (see “label”). |
label | An often artificially constructed term that aims to attach a maximum of unambiguous meaning to an RU. | For example, “Biopsy of head and neck structure” is an unambiguous label but might not be a term that would be commonly used by practitioners. |
concept | No definition given because “concept” is used in numerous, partly contradictory senses [17]. Despite the immense popularity of this word, we recommend always using it with an attribute, e.g., “SNOMED CT concept”, in order to avoid ambiguous interpretations (in which case it is referencing an RU in the associated artifact). | According to the community, it is used in the sense of “entity of thought” (encompassing classes, binary relations, and individuals in SNOMED CT; classes and individuals in UMLS), “information template” (clinical model community), “unary predicate” (logic), “universal” (ontology). |
class | A group of things that share some properties. | Linguistic expressions (terms, labels, scope notes, definitions), as well as formal axioms, describe the class and criteria for membership. Most RUs (nodes) in ontologies denote classes. Synonym: universal |
individual | A single thing in a domain. | Individuals are members of classes.Synonym: particular. |
terminology | An information artifact that includes a “set of designations belonging within a discipline”. | Normally, these designations are “terms”, i.e., units of human language, but codes might also be encompassed. Given the broad use of “terminology” in biomedical informatics as well as in biomedical science (beyond the common use of “terminology” in the terminology world), we recommend always using it together with qualifying adjectives. |
reference terminology | A terminology that organizes RUs in a domain, with human readable, maximally self-explaining labels and potentially formal or textual definitions or scope notes. | Reference terminologies are uncommitted to any specific purpose. Their representational units are often named “concepts”, e.g., SNOMED CT concepts. |
aggregationterminology | A terminology where RUs are systematically organized in single nonoverlapping hierarchies, enhanced by classification rules. | Aggregation terminologies are also known as classifications, e.g., the WHO classifications such as the International Classification of Diseases. Aggregation terminologies are meant for specific purposes like data aggregation and ordering. Synonym: classification. |
user interface terminology | A terminology containing terms used in written and oral communication within specific contexts determined by language, dialect, application, and user groups. | User interface terminologies either lack semantic import altogether or acquire their necessary semantic import by linkage to reference terminologies/ontologies or aggregation terminologies. They are often ambiguous and, therefore, not just alternative labels. User interface terminologies obey less strict organizing principles; terms are grouped topically rather than ontologically. |
thesaurus | An informal terminology that groups together words and terms according to similarity of meaning. | In biomedical informatics, there is a tendency to extend the meaning of the word “ontology” also to thesauri (e.g., MeSH, NCIt, UMLS), which we recommend to avoid. |
Formal ontology | A representational artifact, comprising a taxonomy as proper part, whose representational units are intended to designate some combination of universals, defined classes, and relations between them. | The use of the word “ontology” should be limited to carefully engineered, principled, and computable models of meaning. As such, ontologies can be described as formally founded reference terminologies. Thesauri (e.g., MeSH) or simple data models (i2b2) are not ontologies in our sense. We avoid the use of “ontology” in the sense of the Obrst semantic spectrum [18]. To enhance clarity, we recommend the use of this composed term “formal ontology”. |
Standard | An information artifact developed in community-driven consensus processes that specifies uniform criteria, methods, processes, and/or practices for a certain domain. | The term “standard” is also used in a broader sense for specifications that adopt a “de facto” standard status due to acceptance by a large public or market forces.The term “standard” may be applied (i) to single artifacts, which may be huge (e.g., SNOMED CT) or tiny (a single ISO13606 or openEHR archetype), (ii) processes for creating artifacts that connect into a larger whole, and (iii) shells that support the creation of (i) by (ii) by numerous distributed parties. |
quasi-standard | A specification that is not the outcome of a standardization process but is nevertheless accepted by a larger community. | Due to their large acceptance, quasi-standards can be relatively safely referred to where real standards do not exist.Synonym: de facto standard |
information model | An information artifact for a specific domain like healthcare, by which a bounded set of facts, assertions, and instructions are expressed to meet a specified requirement, typically that of an implementation. | Elements of an information model can be instantiated to create persistent and/or in-memory data. |
reference model | A model that typically defines a logical model of data based on very generic entities (classes) and data types. | In the healthcare domain, reference models are often published as standards [19,20]. |
semantic interoperability | The ability of the flow of information between different components within the same or different systems or institutions to be meaning-preserving. | Semantic interoperability attracts growing attention in the area of biomedical semantics. |
scale | The level of granularity in describing physical entities. | Macroscale: the patient—microscale: genes, proteins, etc. |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Lin, A.Y.; Arabandi, S.; Beale, T.; Duncan, W.D.; Hicks, A.; Hogan, W.R.; Jensen, M.; Koppel, R.; Martínez-Costa, C.; Nytrø, Ø.; et al. Improving the Quality and Utility of Electronic Health Record Data through Ontologies. Standards 2023, 3, 316-340. https://doi.org/10.3390/standards3030023
Lin AY, Arabandi S, Beale T, Duncan WD, Hicks A, Hogan WR, Jensen M, Koppel R, Martínez-Costa C, Nytrø Ø, et al. Improving the Quality and Utility of Electronic Health Record Data through Ontologies. Standards. 2023; 3(3):316-340. https://doi.org/10.3390/standards3030023
Chicago/Turabian StyleLin, Asiyah Yu, Sivaram Arabandi, Thomas Beale, William D. Duncan, Amanda Hicks, William R. Hogan, Mark Jensen, Ross Koppel, Catalina Martínez-Costa, Øystein Nytrø, and et al. 2023. "Improving the Quality and Utility of Electronic Health Record Data through Ontologies" Standards 3, no. 3: 316-340. https://doi.org/10.3390/standards3030023